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We investigate the azimuthal asymmetries such as $\cos2{\phi_T}$ and Sivers symmetry for $J/\psi$ and $\pi^\pm$ production in electron-proton scattering, focusing on scenarios where the $J/\psi$ and the pion are produced in an almost back-to-back configuration. The electron is unpolarized, while the proton can be unpolarized or transversely polarized. For the $J/\psi$ formation, we use non-relativistic QCD (NRQCD), while $\pi^\pm$ is formed due to parton fragmentation. In this kinematics, we utilize the transverse momentum-dependent factorization framework to calculate the cross sections and asymmetries. We consider both quark and gluon-initiated processes and show that the gluon contribution dominates. We provide numerical estimates of the upper bounds on the azimuthal asymmetries, as well as employ a Gaussian parametrization for the gluon transverse momentum distributions (TMDs), within the kinematical region accessible by the upcoming Electron-Ion Collider (EIC)., Comment: Version to appear in PRD

On the advent of the slow death of Moore's law, the silicon industry is moving towards a new era of chiplets. The automotive industry is experiencing a profound transformation towards software-defined vehicles, fueled by the surging demand for automotive compute chips, expected to reach 20-22 billion by 2030. High-performance compute (HPC) chips become instrumental in meeting the soaring demand for computational power. Various strategies, including centralized electrical and electronic architecture and the innovative Chiplet Systems, are under exploration. The latter, breaking down System-on-Chips (SoCs) into functional units, offers unparalleled customization and integration possibilities. The research accentuates the crucial open Chiplet ecosystem, fostering collaboration and enhancing supply chain resilience. In this paper, we address the unique challenges that arise when attempting to leverage chiplet-based architecture to design a holistic silicon solution for the automotive industry. We propose a throughput-oriented micro-architecture for ADAS and infotainment systems alongside a novel methodology to evaluate chiplet architectures. Further, we develop in-house simulation tools leveraging the gem5 framework to simulate latency and throughput. Finally, we perform an extensive design of thermally-aware chiplet placement and develop a micro-fluids-based cooling design.

Preference learning algorithms (e.g., RLHF and DPO) are frequently used to steer LLMs to produce generations that are more preferred by humans, but our understanding of their inner workings is still limited. In this work, we study the conventional wisdom that preference learning trains models to assign higher likelihoods to more preferred outputs than less preferred outputs, measured via ranking accuracy. Surprisingly, we find that most state-of-the-art preference-tuned models achieve a ranking accuracy of less than 60% on common preference datasets. We furthermore derive the idealized ranking accuracy that a preference-tuned LLM would achieve if it optimized the DPO or RLHF objective perfectly. We demonstrate that existing models exhibit a significant alignment gap -- i.e., a gap between the observed and idealized ranking accuracies. We attribute this discrepancy to the DPO objective, which is empirically and theoretically ill-suited to fix even mild ranking errors in the reference model, and derive a simple and efficient formula for quantifying the difficulty of learning a given preference datapoint. Finally, we demonstrate that ranking accuracy strongly correlates with the empirically popular win rate metric when the model is close to the reference model used in the objective, shedding further light on the differences between on-policy (e.g., RLHF) and off-policy (e.g., DPO) preference learning algorithms., Comment: NeurIPS 2024 camera-ready

Neural embedding models have become a fundamental component of modern information retrieval (IR) pipelines. These models produce a single embedding $x \in \mathbb{R}^d$ per data-point, allowing for fast retrieval via highly optimized maximum inner product search (MIPS) algorithms. Recently, beginning with the landmark ColBERT paper, multi-vector models, which produce a set of embedding per data point, have achieved markedly superior performance for IR tasks. Unfortunately, using these models for IR is computationally expensive due to the increased complexity of multi-vector retrieval and scoring. In this paper, we introduce MUVERA (MUlti-VEctor Retrieval Algorithm), a retrieval mechanism which reduces multi-vector similarity search to single-vector similarity search. This enables the usage of off-the-shelf MIPS solvers for multi-vector retrieval. MUVERA asymmetrically generates Fixed Dimensional Encodings (FDEs) of queries and documents, which are vectors whose inner product approximates multi-vector similarity. We prove that FDEs give high-quality $\epsilon$-approximations, thus providing the first single-vector proxy for multi-vector similarity with theoretical guarantees. Empirically, we find that FDEs achieve the same recall as prior state-of-the-art heuristics while retrieving 2-5$\times$ fewer candidates. Compared to prior state of the art implementations, MUVERA achieves consistently good end-to-end recall and latency across a diverse set of the BEIR retrieval datasets, achieving an average of 10$\%$ improved recall with $90\%$ lower latency.

The rapid-pace growing demand for high-performance computation and big-data manipulation entails substantial increase in global power consumption, and challenging thermal management. Thus, there is a need in allocating competitive alternatives for complementary metal-oxide-semiconductor (CMOS) technologies. Superconducting platforms, such as rapid single flux quantum (RSFQ) lack electric resistance and excel in power efficiency and time performance. However, traditional RSFQs require 3D geometry for their Josephson junctions (JJs) imposing a large footprint, and hence preventing device miniaturization and increasing processing time. Here, we demonstrate that RSFQ logic circuits of planar geometry with weak-link bridges are scalable, relatively easy to process and are CMOS-compatible on a Si chip. Universal logic gates, as well as combinational arithmetic circuiting that are based on these devices are demonstrated. The power consumption and processing time of these logic circuits were as low as 0.8 nW and 13 ps, an order of magnitude improvement with respect to the equivalent traditional-RSFQ logic circuits and two orders of magnitude with respect to CMOS. The competitive performance of planar RSFQ logic circuits renders them for promising CMOS substitutes, especially in the supercomputational realm.

Creating multilingual task-oriented dialogue (TOD) agents is challenging due to the high cost of training data acquisition. Following the research trend of improving training data efficiency, we show for the first time, that in-context learning is sufficient to tackle multilingual TOD. To handle the challenging dialogue state tracking (DST) subtask, we break it down to simpler steps that are more compatible with in-context learning where only a handful of few-shot examples are used. We test our approach on the multilingual TOD dataset X-RiSAWOZ, which has 12 domains in Chinese, English, French, Korean, Hindi, and code-mixed Hindi-English. Our turn-by-turn DST accuracy on the 6 languages range from 55.6% to 80.3%, seemingly worse than the SOTA results from fine-tuned models that achieve from 60.7% to 82.8%; our BLEU scores in the response generation (RG) subtask are also significantly lower than SOTA. However, after manual evaluation of the validation set, we find that by correcting gold label errors and improving dataset annotation schema, GPT-4 with our prompts can achieve (1) 89.6%-96.8% accuracy in DST, and (2) more than 99% correct response generation across different languages. This leads us to conclude that current automatic metrics heavily underestimate the effectiveness of in-context learning.

We demonstrate monolithically grown germanium-tin (GeSn) on silicon avalanche photodiodes (APDs) for infrared light detection. A relatively thinner Ge buffer design was adopted to allow effective photo carriers to transport from the GeSn absorber to the Si multiplication layer such that clear punch-through behavior and a saturated primary responsivity of 0.3 A/W at 1550 nm were observed before avalanche breakdown in GeSn/Si APDs for the first time. The spectral response covers 1500 to 1700 nm. The measured punch-through and breakdown voltages are 15 and 17 V, respectively. Undisputed multiplication gain was obtained with the maximum value of 4.5 at 77 K, and 1.4 at 250 K, directly in reference to the saturated primary responsivity from the same device rather than a different GeSn p-i-n photodiode in previous reports. A peak responsivity was measured as 1.12 A/W at 1550 nm and 77 K., Comment: 8 pages, 5 figures, invited paper

Transformers have revolutionized various domains of artificial intelligence due to their unique ability to model long-range dependencies in data. However, they lack in nuanced, context-dependent modulation of features and information flow. This paper introduces two significant enhancements to the transformer architecture - the Evaluator Adjuster Unit (EAU) and Gated Residual Connections (GRC) - designed to address these limitations. The EAU dynamically modulates attention outputs based on the relevance of the input context, allowing for more adaptive response patterns. Concurrently, the GRC modifies the transformer's residual connections through a gating mechanism that selectively controls the information flow, thereby enhancing the network's ability to focus on contextually important features. We evaluate the performance of these enhancements across several benchmarks in natural language processing. Our results demonstrate improved adaptability and efficiency, suggesting that these modifications could set new standards for designing flexible and context-aware transformer models., Comment: 10 pages, 2 figures, 4 experiments

Ordinal Classification (OC) is a widely encountered challenge in Natural Language Processing (NLP), with applications in various domains such as sentiment analysis, rating prediction, and more. Previous approaches to tackle OC have primarily focused on modifying existing or creating novel loss functions that \textbf{explicitly} account for the ordinal nature of labels. However, with the advent of Pretrained Language Models (PLMs), it became possible to tackle ordinality through the \textbf{implicit} semantics of the labels as well. This paper provides a comprehensive theoretical and empirical examination of both these approaches. Furthermore, we also offer strategic recommendations regarding the most effective approach to adopt based on specific settings., Comment: Findings of ACL 2024

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1W) and T2-weighted (T2W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We developed a new pancreas segmentation method, called PanSegNet, combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet's accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (std: 7.2%, at case level) with CT, 85.0% (std: 7.9%) with T1W MRI, and 86.3% (std: 6.4%) with T2W MRI. There was a high correlation for pancreas volume prediction with R^2 of 0.91, 0.84, and 0.85 for CT, T1W, and T2W, respectively. We found moderate inter-observer (0.624 and 0.638 for T1W and T2W MRI, respectively) and high intra-observer agreement scores. All MRI data is made available at https://osf.io/kysnj/. Our source code is available at https://github.com/NUBagciLab/PaNSegNet., Comment: Peer-reviewer version

X-ray is one of the prevalent image modalities for the detection and diagnosis of the human body. X-ray provides an actual anatomical structure of an organ present with disease or absence of disease. Segmentation of disease in chest X-ray images is essential for the diagnosis and treatment. In this paper, a framework for the segmentation of X-ray images using artificial intelligence techniques has been discussed. Here data has been pre-processed and cleaned followed by segmentation using SegNet and Residual Net approaches to X-ray images. Finally, segmentation has been evaluated using well known metrics like Loss, Dice Coefficient, Jaccard Coefficient, Precision, Recall, Binary Accuracy, and Validation Accuracy. The experimental results reveal that the proposed approach performs better in all respect of well-known parameters with 16 batch size and 50 epochs. The value of validation accuracy, precision, and recall of SegNet and Residual Unet models are 0.9815, 0.9699, 0.9574, and 0.9901, 0.9864, 0.9750 respectively., Comment: 10 pages, 3 figures

Owing to its true direct bandgap and tunable bandgap energies,GeSn alloys are increasingly attractive as gain media for mid-IR lasers that can be monolithically integrated on Si. Demonstrations of optically pumped GeSn laser at room under pulsed condition and at cryogenic temperature under continuous-wave excitation show great promise of GeSn lasers to be efficient electrically injected light sources on Si. Here we report electrically injected GeSn lasers using Fabry-Perot cavity with 20, 40, and 80 micron ridge widths. A maximum operating temperature of 140 K with lasing threshold of 0.756 kA/cm2 at 77 K and emitting wavelength of 2722 nm at 140 K was obtained. The lower threshold current density compared to previous works was achieved by reducing optical loss and improving the optical confinement. The peak power was measured as 2.2 mW/facet at 77 K.

The presence of muscles throughout the active parts of the body such as the upper and lower limbs, makes electromyography-based human-machine interaction prevalent. However, muscle signals are stochastic and noisy. These noises can be regular and irregular. Irregular noises due to movements or electrical switching require dynamic filtering. Conventionally, filters are stacked, which trims and delays the signal unnecessarily. This study introduces a decontamination technique involving a supervised rewarding strategy to drive a deep Q-network-based agent (supDQN). It applies one of three filters to decontaminate a 1sec long surface electromyography signal, which is dynamically contaminated. A machine learning agent identifies whether the signal after filtering is clean or noisy. Accordingly, a reward is generated. The identification accuracy is enhanced by using a local interpretable model-agnostic explanation. The deep Q-network is guided by this reward to select filter optimally while decontaminating a signal. The proposed filtering strategy is tested on four noise levels (-5 dB, -1 dB, +1 dB, +5 dB). supDQN filters the signal desirably when the signal-to-noise ratio (SNR) is between -5 dB to +1 dB. It filters less desirably at high SNR (+5 dB). A normalized root mean square (nRMSE) is formulated to depict the difference of filtered signal from ground truth. This is used to compare supDQN and conventional methods including wavelet denoising with debauchies and symlet wavelet, high order low pass filter, notch filter, and high pass filter. The proposed filtering strategy gives an average value nRMSE of 1.1974, which is lower than the conventional filters.

We develop an action formalism to calculate probabilities of rare events in cluster-cluster aggregation for arbitrary collision kernels and establish a pathwise large deviation principle with total mass being the rate. As an application, the rate function for the number of surviving particles as well as the optimal evolution trajectory are calculated exactly for the constant, sum and product kernels. For the product kernel, we argue that the second derivative of the rate function has a discontinuity. The theoretical results agree with simulations tailored to the calculation of rare events.

Artificial intelligence (AI) has made significant advances in recent years and opened up new possibilities in exploring applications in various fields such as biomedical, robotics, education, industry, etc. Among these fields, human hand gesture recognition is a subject of study that has recently emerged as a research interest in robotic hand control using electromyography (EMG). Surface electromyography (sEMG) is a primary technique used in EMG, which is popular due to its non-invasive nature and is used to capture gesture movements using signal acquisition devices placed on the surface of the forearm. Moreover, these signals are pre-processed to extract significant handcrafted features through time and frequency domain analysis. These are helpful and act as input to machine learning (ML) models to identify hand gestures. However, handling multiple classes and biases are major limitations that can affect the performance of an ML model. Therefore, to address this issue, a new mixture of experts extra tree (MEET) model is proposed to identify more accurate and effective hand gesture movements. This model combines individual ML models referred to as experts, each focusing on a minimal class of two. Moreover, a fully trained model known as the gate is employed to weigh the output of individual expert models. This amalgamation of the expert models with the gate model is known as a mixture of experts extra tree (MEET) model. In this study, four subjects with six hand gesture movements have been considered and their identification is evaluated among eleven models, including the MEET classifier. Results elucidate that the MEET classifier performed best among other algorithms and identified hand gesture movement accurately.

Informed sampling techniques improve the convergence rate of sampling-based planners by guiding the sampling toward the most promising regions of the problem domain, where states that can improve the current solution are more likely to be found. However, while this approach significantly reduces the planner's exploration space, the sampling subset may still be too large if the current solution contains redundant states with many twists and turns. This article addresses this problem by introducing a greedy version of the informed set that shrinks only based on the maximum heuristic cost of the state along the current solution path. Additionally, we present Greedy RRT* (G-RRT*), a bi-directional version of the anytime Rapidly-exploring Random Trees algorithm that uses this greedy informed set to focus sampling on the promising regions of the problem domain based on heuristics. Experimental results on simulated planning problems, manipulation problems on Barrett WAM Arms, and on a self-reconfigurable robot, Panthera, show that G-RRT* produces asymptotically optimal solution paths and outperforms state-of-the-art RRT* variants, especially in high dimensions.

Spiking Neural Networks (SNNs), recognized for their biological plausibility and energy efficiency, employ sparse and asynchronous spikes for communication. However, the training of SNNs encounters difficulties coming from non-differentiable activation functions and the movement of spike-based inter-layer data. Spike-Timing Dependent Plasticity (STDP), inspired by neurobiology, plays a crucial role in SNN's learning, but its still lacks the conscious part of the brain used for learning. Considering the issue, this research work proposes a Consciousness Driven STDP (CD-STDP), an improved solution addressing inherent limitations observed in conventional STDP models. CD-STDP, designed to infuse the conscious part as coefficients of long-term potentiation (LTP) and long-term depression (LTD), exhibit a dynamic nature. The model connects LTP and LTD coefficients to current and past state of synaptic activities, respectively, enhancing consciousness and adaptability. This consciousness empowers the model to effectively learn while understanding the input patterns. The conscious coefficient adjustment in response to current and past synaptic activity extends the model's conscious and other cognitive capabilities, offering a refined and efficient approach for real-world applications. Evaluations on MNIST, FashionMNIST and CALTECH datasets showcase $CD$-STDP's remarkable accuracy of 98.6%, 85.61% and 99.0%, respectively, in a single hidden layer SNN. In addition, analysis of conscious elements and consciousness of the proposed model on SNN is performed., Comment: 10 pages, 5 figures, Journal

We present a tunable magnetoelastic lattice with a multistable onsite potential, focusing on a tristable potential. Through experimental and numerical analysis, we verify the existence of three types of transition waves with distinct amplitudes and velocities. Additionally, we establish the presence of a scaling law that elucidates various characteristics of these transition waves. By manipulating the onsite potential, we investigate the collision dynamics of two transition waves within the system. In chains featuring an asymmetric potential well, the collision of similar transition waves leads to the remote nucleation of a new phase. In chains with a symmetric potential well, the collision of dissimilar transition waves results in the formation of a stationary domain wall

We investigate the measurement correlation between the effective spin and the effective tidal deformability in gravitational wave signals from binary neutron star mergers. To efficiently measure the effective tidal deformability parameter, we exploit the fact that the tidal effects in a binary system are prominent when the components are closer during the late-inspiral. Thus, we indicate to a computationally efficient strategy of extracting the tidal information compressed within seconds before the merger. We report our observations for \texttt{GW170817} and explore the suitability of our approach in the upcoming observation scenarios of the current and the future ground-based observatories. Fast and accurate measurements of the tidal deformability parameters can be used to inform astronomers in prioritizing the electromagnetic follow-up efforts for such sources.

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.

We consider the PageRank problem in the dynamic setting, where the goal is to explicitly maintain an approximate PageRank vector $\pi \in \mathbb{R}^n$ for a graph under a sequence of edge insertions and deletions. Our main result is a complete characterization of the complexity of dynamic PageRank maintenance for both multiplicative and additive ($L_1$) approximations. First, we establish matching lower and upper bounds for maintaining additive approximate PageRank in both incremental and decremental settings. In particular, we demonstrate that in the worst-case $(1/\alpha)^{\Theta(\log \log n)}$ update time is necessary and sufficient for this problem, where $\alpha$ is the desired additive approximation. On the other hand, we demonstrate that the commonly employed ForwardPush approach performs substantially worse than this optimal runtime. Specifically, we show that ForwardPush requires $\Omega(n^{1-\delta})$ time per update on average, for any $\delta > 0$, even in the incremental setting. For multiplicative approximations, however, we demonstrate that the situation is significantly more challenging. Specifically, we prove that any algorithm that explicitly maintains a constant factor multiplicative approximation of the PageRank vector of a directed graph must have amortized update time $\Omega(n^{1-\delta})$, for any $\delta > 0$, even in the incremental setting, thereby resolving a 13-year old open question of Bahmani et al.~(VLDB 2010). This sharply contrasts with the undirected setting, where we show that $\rm{poly}\ \log n$ update time is feasible, even in the fully dynamic setting under oblivious adversary.

In this article, we investigate the impact of cosmological parameters on black holes using an exact solution to Einstein's equations that satisfies the Whittaker equation of state. We examine a spherically symmetric black hole in the background of a static Einstein Universe with a perfect fluid source with the cosmological constant. This solution is characterized by two independent parameters, namely the size of the universe~($R$) and the cosmological constant~(Lambda), representing the cosmological influences. We explore phenomena such as periastron precession and the scattering of massless scalar fields to determine how these cosmological parameters affect the physics around black holes.

Average linkage Hierarchical Agglomerative Clustering (HAC) is an extensively studied and applied method for hierarchical clustering. Recent applications to massive datasets have driven significant interest in near-linear-time and efficient parallel algorithms for average linkage HAC. We provide hardness results that rule out such algorithms. On the sequential side, we establish a runtime lower bound of $n^{3/2-\epsilon}$ on $n$ node graphs for sequential combinatorial algorithms under standard fine-grained complexity assumptions. This essentially matches the best-known running time for average linkage HAC. On the parallel side, we prove that average linkage HAC likely cannot be parallelized even on simple graphs by showing that it is CC-hard on trees of diameter $4$. On the possibility side, we demonstrate that average linkage HAC can be efficiently parallelized (i.e., it is in NC) on paths and can be solved in near-linear time when the height of the output cluster hierarchy is small., Comment: To appear at ICALP 2024

We demonstrate a high-NA (0.88), ultra-low-f-number (f/0.2714), multi-wavelength (480nm, 550nm and 650nm) multilevel diffractive MicroLens Array (MLA) using inverse design. Each microlens in the array is close-packed with diameter of 70 {\mu}m and focal length of only 19 {\mu}m in air. The MLA was patterned on one surface of a polymer film via UV casting, such that the focal plane was located on the distal end of the film (n of polymer ~ 1.47, thickness = 28 {\mu}m, effective f/# (NA) inside polymer ~ 0.4 (0.78)). Each microlens focuses incident light at 3 design wavelengths into a focal spot with measured full-width at half-maximum (FWHM) < 1 {\mu}m. By placing this MLA directly on a high-resolution print, we demonstrated RGB integral imaging with applications in document security. Compared to refractive MLAs, our diffractive MLA reduces the thickness by > 3X, which is advantageous for manufacturability. Since these multi-level diffractive MLAs are fabricated using UV-casting, they have the potential for low-cost, high-volume manufacturing.

The intricate interplay between spin and lattice degrees of freedom in two-dimensional magnetic materials plays a pivotal role in modifying their magnetic characteristics, engendering hybrid quasiparticles, and implementing functional devices. Herein, we present our comprehensive and in-depth investigations on magnetic and lattice excitations of MnPSe3-xSx (x = 0, 0.5, and 1.5) alloys, utilizing temperature- and polarization-dependent Raman scattering. Our experimental results reveal the occurrence of multiple phase transitions, evidenced by notable changes in phonon self-energy and the appearance or splitting of phonon modes. These emergent phases are tied to the development of long and short-range spin-spin correlations, as well as to spin reorientations or magnetic instabilities. Our analysis of two-magnon excitations as a function of temperature and composition showcases their hybridization with phonons whose degree weakens with increasing x. Moreover, the suppression of spin-dependent phonon intensity in chemically most-disordered MnPSe3-xSx (x = 1.5) suggests that chalcogen substitution offers a control knob of tuning spin and phonon dynamics by modulating concurrently superexchange pathways and a degree of trigonal distortions.

Attention deficit hyperactivity disorder (ADHD) is one of the common neurodevelopmental disorders in children. This paper presents an automated approach for ADHD detection using the proposed entropy difference (EnD)- based encephalogram (EEG) channel selection approach. In the proposed approach, we selected the most significant EEG channels for the accurate identification of ADHD using an EnD-based channel selection approach. Secondly, a set of features is extracted from the selected channels and fed to a classifier. To verify the effectiveness of the channels selected, we explored three sets of features and classifiers. More specifically, we explored discrete wavelet transform (DWT), empirical mode decomposition (EMD) and symmetrically-weighted local binary pattern (SLBP)-based features. To perform automated classification, we have used k-nearest neighbor (k-NN), Ensemble classifier, and support vectors machine (SVM) classifiers. Our proposed approach yielded the highest accuracy of 99.29% using the public database. In addition, the proposed EnD-based channel selection has consistently provided better classification accuracies than the entropy-based channel selection approach. Also, the developed method

Fluctuations in the energy gap and coupling constants in and between chromophores can play important role in the absorption and energy transfer across a collection of two level systems. In a noisy environment, fluctuations can control efficiency of energy transfer through several factors, including quantum coherence. Several recent studies have investigated the impact of light-induced stationary quantum coherence on the efficiency of transferring optical excitation to a designated "trap" state, crucial for subsequent reactions such as those in photosynthesis. However, these studies have typically employed either a Markovian, or a perturbative approximation for the environment induced fluctuations. In this study, we depart from these approaches to incorporate memory effects by using Kubo's quantum stochastic Liouville equation (QSLE). We introduce the effects of the decay of excitation (to the ground state) and the desired trapping that provides the direction of the motion of the excitation. In the presence of light-induced pumping, we establish a relation between the mean survival time, efficiency, and the correlation decay time of the bath-induced fluctuations. We find a decrease in the steady state coherence during the transition from the non-Markovian regime (characterized by small values of fluctuation strength V and inverse of bath correlation time b) to the Markovian limit (where V and b are both large), resulting in a decrease in efficiency. We recover a connection between transfer flux and the imaginary part of coherences in both equilibrium and excited bath states, in both correlated and We uncover a non-monotonic dependence of efficiency on site energy heterogeneity for both correlated and uncorrelated bath models., Comment: 27 Pages, 3 figures

Online social media has become increasingly popular in recent years due to its ease of access and ability to connect with others. One of social media's main draws is its anonymity, allowing users to share their thoughts and opinions without fear of judgment or retribution. This anonymity has also made social media prone to harmful content, which requires moderation to ensure responsible and productive use. Several methods using artificial intelligence have been employed to detect harmful content. However, conversation and contextual analysis of hate speech are still understudied. Most promising works only analyze a single text at a time rather than the conversation supporting it. In this work, we employ a tree-based approach to understand how users behave concerning toxicity in public conversation settings. To this end, we collect both the posts and the comment sections of the top 100 posts from 8 Reddit communities that allow profanity, totaling over 1 million responses. We find that toxic comments increase the likelihood of subsequent toxic comments being produced in online conversations. Our analysis also shows that immediate context plays a vital role in shaping a response rather than the original post. We also study the effect of consensual profanity and observe overlapping similarities with non-consensual profanity in terms of user behavior and patterns.

Introduction: Music provides an incredible avenue for individuals to express their thoughts and emotions, while also serving as a delightful mode of entertainment for enthusiasts and music lovers. Objectives: This paper presents a comprehensive approach to enhancing the user experience through the integration of emotion recognition, music recommendation, and explainable AI using GRAD-CAM. Methods: The proposed methodology utilizes a ResNet50 model trained on the Facial Expression Recognition (FER) dataset, consisting of real images of individuals expressing various emotions. Results: The system achieves an accuracy of 82% in emotion classification. By leveraging GRAD-CAM, the model provides explanations for its predictions, allowing users to understand the reasoning behind the system's recommendations. The model is trained on both FER and real user datasets, which include labelled facial expressions, and real images of individuals expressing various emotions. The training process involves pre-processing the input images, extracting features through convolutional layers, reasoning with dense layers, and generating emotion predictions through the output layer. Conclusion: The proposed methodology, leveraging the Resnet50 model with ROI-based analysis and explainable AI techniques, offers a robust and interpretable solution for facial emotion detection paper.

Nanopatterned magnetic thin films offer a platform for exploration of tailored magnetic properties such as emergent long-range order. A prominent example is artificial spin ice (ASI), where an arrangement of nanoscale magnetic elements, acting as macrospins, interact via their dipolar fields. In this study, we discuss the transition from antiferromagnetic (AF) to ferromagnetic (FM) long-range order in a square lattice ASI, as the magnetic elements are gradually rotated through 45{\deg} to a "pinwheel" configuration. The AF$\unicode{x2013}$FM transition is observed experimentally using synchrotron radiation x-ray spectromicroscopy and occurs for a critical rotation angle of the nanomagnets, contingent upon the dipolar coupling determined by their separation in the lattice. Large-scale magnetic dipole simulations show that the point-dipole approximation fails to capture the correct AF$\unicode{x2013}$FM transition angle. However, excellent agreement with experimental data is obtained using a dumbbell-dipole model which better reflects the actual dipolar fields of the magnets. This model explains the coupling-dependence of the transition angle, another feature not captured by the pointdipole model. Our findings resolve a discrepancy between measurement and theory in previous work on "pinwheel" ASIs. Control of the AF$\unicode{x2013}$FM transition and this revised model open for improved design of magnetic order in nanostructured systems., Comment: 8 pages, 6 figures

This paper presents our submission to the SemEval 2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure. We present two approaches to solving the task of legal answer validation, given an introduction to the case, a question and an answer candidate. Firstly, we fine-tuned pre-trained BERT-based models and found that models trained on domain knowledge perform better. Secondly, we performed few-shot prompting on GPT models and found that reformulating the answer validation task to be a multiple-choice QA task remarkably improves the performance of the model. Our best submission is a BERT-based model that achieved the 7th place out of 20.

Human Activity Recognition is a subject of great research today and has its applications in remote healthcare, activity tracking of the elderly or the disables, calories burnt tracking etc. In our project, we have created an Android application that recognizes the daily human activities and calculate the calories burnt in real time. We first captured labeled triaxial acceleration readings for different daily human activities from the smartphone's embedded accelerometer. These readings were preprocessed using a median filter. 42 features were extracted using various methods. We then tested various machine learning algorithms along with dimensionality reduction. Finally, in our Android application, we used the machine learning algorithm and a subset of features that provided maximum accuracy and minimum model building time. This is used for real-time activity recognition and calculation of calories burnt using a formula based on Metabolic Equivalent.

We investigate a higher-order topological insulator (HOTI) under strong nonlinearity, focusing on the existence and stability of high-amplitude corner states, which can find applications in optics, acoustics, elastodynamics, and other wave-based systems. Our study centers on a breathing Kagome lattice composed of point masses and springs known to exhibit edge and corner states in its linear regime. By introducing onsite cubic nonlinearity, we analyze its impact on both edge and corner states. The nonlinear continuation of the corner state unveils stable high-amplitude corner states within the lattice, featuring non-zero displacements at even sites from the corner -- a characteristic absent in the linear limit. Interestingly, the nonlinear continuation of the edge state reveals its transformation into distinct families of high-amplitude corner states via two pitchfork bifurcations. While some states maintain stability, others become unstable through real instability and Neimark-Sacker bifurcation. These unstable corner states dissipate their energy into the edges and the bulk over an extended period, as corroborated by long-time dynamical simulations. Consequently, our study provides insights into achieving significant energy localization at the corners of HOTIs through various classes of nonlinear states., Comment: 11 pages, 7 figures

We present a framework to quantify the clustering of gravitational wave (GW) transient sources and measure their spatial cross-correlation with the large-scale structure (LSS) of the universe using the $k$-nearest neighbour ($k$NN) formalism. As a first application, we measure the nearest-neighbour distributions of 53 suitably selected Binary Black Hole (BBH) mergers detected in the first three observation runs of LIGO-Virgo-KAGRA and cross-correlate these sources with ~$1.7 \times 10^7$ galaxies and quasars from the WISE$\times$SuperCOSMOS all-sky catalogue. To determine the significance of the clustering signal while accounting for observational systematics in the GW data, we create 135 realisations of mock BBHs that are statistically similar to the observed BBHs but spatially unclustered. We find no evidence for spatial clustering or cross-correlation with LSS in the data and conclude that the present sky localisation and number of detections are insufficient to get a statistically significant clustering signal. Looking forward, the statistically large number of detections and the significant improvements in sky localisations expected from future observing runs of LIGO (including LIGO India) and the next generation of GW detectors will enable measurement of the BBH-LSS cross-correlation and open a new window into cosmology., Comment: 21 pages, 20 figures including 2 figures in the appendix

Stress recognition through physiological signals such as Electrocardiogram (ECG) signals has garnered significant attention. Traditionally, research in this field predominantly focused on utilizing handcrafted features or raw signals as inputs for learning algorithms. However, there is now a burgeoning interest within the community in leveraging large-scale vision foundation models (VFMs) like ResNet50, VGG19, and others. These VFMs are increasingly preferred due to their ability to capture complex features, enhancing the accuracy and effectiveness of stress recognition systems. However, no particular focus has been given on combining these VFMs. The combination of VFMs offers promising benefits by harnessing their collective knowledge to extract richer representations for improved stress recognition. So, to mitigate this research gap, we focus on combining different VFMs for stress recognition from ECG and propose SONIC, a novel framework that combines VFMs through their logits and training a fully connected network on the combined logits. Through extensive experimentation, SONIC showed the top performance against individual VFMs performance on the WESAD benchmark. With SONIC, we report state-of-the-art (SOTA) performance in WESAD with 99.36% and 99.24% (stress vs non-stress) and 97.66% and 97.10% (amusement vs stress vs baseline) in accuracy and F1 respectively.

In this work, we investigate multilingual speech Pre-Trained models (PTMs) for Audio deepfake detection (ADD). We hypothesize that multilingual PTMs trained on large-scale diverse multilingual data gain knowledge about diverse pitches, accents, and tones, during their pre-training phase and making them more robust to variations. As a result, they will be more effective for detecting audio deepfakes. To validate our hypothesis, we extract representations from state-of-the-art (SOTA) PTMs including monolingual, multilingual as well as PTMs trained for speaker and emotion recognition, and evaluated them on ASVSpoof 2019 (ASV), In-the-Wild (ITW), and DECRO benchmark databases. We show that representations from multilingual PTMs, with simple downstream networks, attain the best performance for ADD compared to other PTM representations, which validates our hypothesis. We also explore the possibility of fusion of selected PTM representations for further improvements in ADD, and we propose a framework, MiO (Merge into One) for this purpose. With MiO, we achieve SOTA performance on ASV and ITW and comparable performance on DECRO with current SOTA works., Comment: Accepted to NAACL (Findings) 2024

The importance of the Allee effect in studying extinction vulnerability is widely recognized by researchers, and neglecting it could adversely impact the management of threatened or exploited populations [1]. In this article, we examine a discrete predator-prey model where the prey population is associated with two component Allee effects. We derive sufficient conditions for the existence and local stability nature of the fixed points of the system. The occurrence of Neimark-Sacker bifurcation is established, and sufficient conditions are obtained along with the normal form. Numerically, we demonstrate that the system exhibits Neimark-Sacker bifurcation for various system parameters. Additionally, the numerical simulations indicate that certain system parameters have threshold values, above or below which the populations are driven to extinction due to the effect of the double Allee effect.

The worm gearbox is a high-speed transmission system that plays a vital role in various industries. Therefore it becomes necessary to develop a robust fault diagnosis scheme for worm gearbox. Due to advancements in sensor technology, researchers from academia and industries prefer deep learning models for fault diagnosis purposes. The optimal selection of hyperparameters (HPs) of deep learning models plays a significant role in stable performance. Existing methods mainly focused on manual tunning of these parameters, which is a troublesome process and sometimes leads to inaccurate results. Thus, exploring more sophisticated methods to optimize the HPs automatically is important. In this work, a novel optimization, i.e. amended gorilla troop optimization (AGTO), has been proposed to make the convolutional neural network (CNN) adaptive for extracting the features to identify the worm gearbox defects. Initially, the vibration and acoustic signals are converted into 2D images by the Morlet wavelet function. Then, the initial model of CNN is developed by setting hyperparameters. Further, the search space of each Hp is identified and optimized by the developed AGTO algorithm. The classification accuracy has been evaluated by AGTO-CNN, which is further validated by the confusion matrix. The performance of the developed model has also been compared with other models. The AGTO algorithm is examined on twenty-three classical benchmark functions and the Wilcoxon test which demonstrates the effectiveness and dominance of the developed optimization algorithm. The results obtained suggested that the AGTO-CNN has the highest diagnostic accuracy and is stable and robust while diagnosing the worm gearbox.

The vibration analysis of the bearing is very crucial because of its non-stationary nature and low signal-to-noise ratio. Therefore, a novel scheme for detecting bearing defects is put forward based on the extraction of single-valued neutrosophic cross-entropy (SVNCE) to address this issue. Initially, the artificial hummingbird algorithm (AHA) is used to make the feature mode decomposition (FMD) adaptive by optimizing its parameter based on a newly developed health indicator (HI) i.e. sparsity impact measure index (SIMI). This HI ensures full sparsity and impact properties simultaneously. The raw signals are disintegrated into different modes by adaptive FMD at optimal values of its parameters. The energy of these modes is calculated for different health conditions. The energy interval range has been decided based on energy eigen which are then transformed into single-valued neutrosophic sets (SVNSs) for unknown defect conditions. The minimum argument principle employs the least SVNCE values between SVNSs of testing samples (obtained from unknown bearing conditions) and SVNSs of training samples (obtained from known bearing conditions) to recognize the different defects in the bearing. It has been discovered that the suggested methodology is more adept at identifying the various bearing defects.

Rhymes and poems are a powerful medium for transmitting cultural norms and societal roles. However, the pervasive existence of gender stereotypes in these works perpetuates biased perceptions and limits the scope of individuals' identities. Past works have shown that stereotyping and prejudice emerge in early childhood, and developmental research on causal mechanisms is critical for understanding and controlling stereotyping and prejudice. This work contributes by gathering a dataset of rhymes and poems to identify gender stereotypes and propose a model with 97% accuracy to identify gender bias. Gender stereotypes were rectified using a Large Language Model (LLM) and its effectiveness was evaluated in a comparative survey against human educator rectifications. To summarize, this work highlights the pervasive nature of gender stereotypes in literary works and reveals the potential of LLMs to rectify gender stereotypes. This study raises awareness and promotes inclusivity within artistic expressions, making a significant contribution to the discourse on gender equality., Comment: Accepted to appear at LREC-COLING 2024

Population balance models often integrate fundamental kernels, including sum, gelling and Brownian aggregation kernels. These kernels have demonstrated extensive utility across various disciplines such as aerosol physics, chemical engineering, astrophysics, pharmaceutical sciences and mathematical biology for the purpose of elucidating particle dynamics. The objective of this study is to refine the semi-analytical solutions derived from current methodologies in addressing the nonlinear aggregation and coupled aggregation-breakage population balance equation. This work presents a unique semi-analytical approach based on the homotopy analysis method (HAM) to solve pure aggregation and couple aggregation-fragmentation population balance equations, which is an integro-partial differentia equation. By decomposing the non-linear operator, we investigate how to utilize the convergence control parameter to expedite the convergence of the HAM solution towards its precise values in the proposed method.

The non-linear collision-induced breakage equation has significant applications in particulate processes. Two semi-analytical techniques, namely homotopy analysis method (HAM) and accelerated homotopy perturbation method (AHPM) are investigated along with the well-known finite volume method (FVM) to comprehend the dynamical behavior of the non-linear system, i.e., the concentration function, the total number and the total mass of the particles in the system. The theoretical convergence analyses of the series solutions of HAM and AHPM are discussed. In addition, the error estimations of the truncated solutions of both methods equip the maximum absolute error bound. To justify the applicability and accuracy of these methods, numerical simulations are compared with the findings of FVM and analytical solutions considering three physical problems.